4/30/2023 0 Comments Parquet file viewerWe see, first, a summary of the number of columns, rows and format version and the size in bytes. To demonstrate it we are going to execute the command parquet-tools inspect test1.parquet.īelow we see how the tool shows us the schema of the data contained in the file organized by columns. In this example you can see the same dataset represented in parquet and csv format.Įarlier we mentioned that another differentiating feature of parquet versus CSV is that the former includes the schema of the data inside. To illustrate a simple example we can use parquet-tools in Python. However, there are multiple tools to handle parquet files. Who has never imported a CSV into a program and found that the data is misinterpreted (numbers as text, dates as numbers, etc.)?Īs we have already mentioned, one of the disadvantages of parquet compared to CSV is that we cannot open it just by using a text editor. In this way, any program used to read the data can access this metadata, for example, to determine unambiguously what type of data is expected to be read in a given column. are included in the file itself along with the data as such. That is, properties (or metadata) of the data such as the type (whether it is an integer, a real or a string), the number of values, the type of compression (data can be compressed to save space), etc. Otherwise, if we want to store data with the objective of reading many complete rows very often, the parquet format will penalize us in those reads and we will not be efficient since we are using column orientation to read rows.Īnother feature of Parquet is that it is a self-describing data format that embeds the schema or structure within the data itself. As the data in each column is expected to be homogeneous (of the same type), the parquet format opens endless possibilities when it comes to encoding, compressing and optimizing data storage. When using Parquet format, each column is accessible independently from the rest. Although we only want to access the information of one column, because of the format type, we inevitably have to read all the rows of the table. The most extreme difference is noticed when, in a CSV file, we want to read only one column. In Parquet, however, it is each column that is stored independently. Such is the case of CSV, TSV or AVRO formats.īut what does it mean for a data format to be row-oriented or column-oriented? In a CSV file (remember, row-oriented) each record is a row. As you may have guessed, there are other row-oriented formats. The parquet format is a type of column-oriented file format. parquet extension and unlike a CSV, it is not a plain text file (it is represented in binary form), which means that we cannot open and examine it with a simple text editor. Although it may seem obvious, parquet files have a. The Parquet format is a file type that contains data (table type) inside it, similar to the CSV file type. We know that you may have never heard of the Apache Parquet file format before. Let's get started! What is Apache Parquet? Since 2015, Apache Parquet is one of the flagship projects sponsored and maintained by Apache Software Foundation (ASF). The first versions of Apache Parquet were released in 2013. To discuss this topic, in this post we bring you a reflection on the Apache Parquet or simply Parquet data format. A good decision in terms of data format types can be vital with respect to the future scalability of a data-driven solution. We talk about efficiency in terms of processing times, but also in terms of occupied space and, of course, storage costs. Those of us who work in the data sector know the importance of efficiency in multiple aspects of data solutions and architectures. However, it is important to highlight some best practices related to our data formats if we want to design truly efficient and scalable big data solutions. Things have changed a lot since then, and we now use higher-level tools to build solutions based on big data payloads. It's been a long time since we first heard about the Apache Hadoop ecosystem for distributed data processing.
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